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The Role of Artificial Intelligence in Big Data and Data Science
AI and Big Data: Capturing and Analyzing Large Datasets
Artificial Intelligence (AI) has become a cornerstone in the realm of big data, facilitating the capture, structuring, and analysis of vast datasets. AI technologies are employed to extract key insights from big data, enabling industries to make informed decisions and optimize operations1. The integration of AI with big data technologies has empowered AI systems to handle complex data more efficiently, leading to significant advancements in various fields6.
Data-Centric AI: Enhancing Data Quality and Quantity
The concept of data-centric AI has emerged as a critical focus area, emphasizing the importance of high-quality and abundant data for building robust machine learning models. Researchers and practitioners are increasingly shifting their attention from merely advancing model design to improving the data itself. This approach involves developing training data, inference data, and maintaining data quality throughout the AI lifecycle2. By systematically engineering data, AI systems can achieve better performance and reliability.
Human-AI Collaboration in Data Science
The advent of AutoAI or AutoML technologies aims to automate many aspects of data science, including data ingestion, preprocessing, feature engineering, and model creation. Despite concerns about job automation, data scientists believe that the future of their field will involve a collaborative effort between humans and AI systems. This collaboration leverages the strengths of both automation and human expertise to enhance data science practices3.
AI in Healthcare and Pharmaceutical Research
AI has made significant strides in healthcare and pharmaceutical research, particularly in disease diagnosis, drug discovery, and personalized treatment. Advanced AI technologies such as deep learning and neural networks are extensively used to predict disease outbreaks and improve clinical trial designs. These technologies enable rapid and cost-effective research, ultimately providing better healthcare services to the public9.
Explainable AI: Data-Driven and Knowledge-Aware Methods
As AI models become more complex, explaining their decisions has become increasingly challenging. Explainable AI (XAI) aims to provide transparency by utilizing data-driven and knowledge-aware methods. These methods extract causal information and explanatory scenarios from the data itself, making AI systems more interpretable and trustworthy. The development of XAI is crucial for gaining user trust and ensuring the ethical deployment of AI technologies5.
AI for Brain Diseases: Clinical Applications and Challenges
In the domain of brain care, AI has shown promise in diagnosing, planning treatments, and predicting outcomes for various brain diseases. Techniques such as artificial neural networks, support vector machines, and random forests are commonly used to analyze brain images and other medical data. However, challenges remain in gathering comprehensive data and developing explainable AI algorithms to improve clinical decision-making4.
AI on a Chip: Integrating AI with Lab-on-a-Chip Technology
The integration of AI with lab-on-a-chip technology has revolutionized data collection and analysis. This synergy allows for high-throughput, automated, and cost-effective data processing, which is essential for constructing functional AI algorithms. Lab-on-a-chip platforms can generate and analyze complex datasets, making them invaluable for applications in drug design, material discovery, and medical diagnostics10.
Conclusion
AI's role in big data and data science is multifaceted, encompassing data capture, analysis, and the enhancement of data quality. The collaboration between humans and AI, along with advancements in explainable AI and lab-on-a-chip technology, is driving the field forward. As AI continues to evolve, its applications in healthcare, pharmaceuticals, and other industries will expand, offering new opportunities and addressing existing challenges.
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Most relevant research papers on this topic
Artificial Intelligence and Big Data
AI can facilitate big data capture, structuring, and analysis, providing key insights for businesses and industries.
Highly CitedData-centric Artificial Intelligence: A Survey
Data-centric AI emphasizes the importance of data quality and quantity for building machine learning models, and this survey provides a comprehensive overview of various data-centric tasks and methods.
Highly Cited
PreprintHuman-AI Collaboration in Data Science
AutoAI will impact data science practices, but data scientists remain optimistic about job security due to the future of collaboration between humans and AI systems.
Highly CitedArtificial intelligence for brain diseases: A systematic review
AI has the potential to improve diagnosis, planning, and outcome prediction in brain diseases, but requires comprehensive data and explainable algorithms for practical use.
Systematic ReviewHighly CitedA Survey of Data-Driven and Knowledge-Aware eXplainable AI
This survey categorizes existing AI explanation methods into data-driven and knowledge-aware approaches, highlighting their contribution, technical essence, and comparative characteristics.
Systematic ReviewHighly CitedArtificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda
AI decision-making in the era of Big Data faces challenges and offers twelve research propositions for information systems researchers to advance AI research and development.
Highly CitedArtificial intelligence and statistics
The PQRS workflow effectively integrates statistical ideas with human input, improving reproducibility and interpretability of AI products and researches, such as self-driving cars and automated medical diagnoses.
Highly CitedA Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence
Artificial intelligence (AI) has evolved from a primitive concept to a complex, flexible system capable of achieving goals and tasks through flexible adaptation.
Highly CitedArtificial Intelligence in Pharmaceutical and Healthcare Research
AI technologies, particularly deep learning and neural networks, can revolutionize healthcare and pharmaceutical research, improving disease diagnosis, personalized treatment, drug discovery, and epidemic forecasting.
Systematic ReviewHighly Cited